Field of the Invention
The present invention is related to allocating shared resources and more particularly to automatically adjusting resource allocation and automatic demand prediction in real time for highly interactive applications based on non-intentional haptic feedback.
Background Description
Typically, provisioning and managing shared information technology (IT) and especially cloud infrastructure resources involves scheduling jobs according to deadlines, allocating resources to scheduled jobs, setting job priorities and predicting load and performance to maximize utilization. Job scheduling management is described, for example, by Feitelson, “Parallel job scheduling—a status report,” Proceedings of JSSPP, 2005; and by Takefusa et al. “A Study of Deadline Scheduling for Client-Server Systems on the Computational Grid,” Proceedings of HPDC, 2001.
In allocating resources and, further, in determining expected consolidation opportunities, factors considered may include, for example, resource utilization, application response time and energy consumption. Load estimates indicate the typical predicted user load, which varies with actual use over time, depending on how each user interacts with a respective application. Several well known load prediction techniques are available, some of which consider user device interactions independent of whether allocation may be improved. Typically, however, service providers have monitored device requests on the provider (server) side to measure the degree of user interaction with cloud based applications. Utilization estimates project how many users are expected to utilize a particular service over time.
While these techniques may work well with stationary or for pseudo-stationary clients, client mobility can render these techniques ineffective. Mobile client devices typically run local applications that manage digital content consumption. State of the art mobile devices, such as stand alone, handheld multimedia players, tablet computers, personal digital assistants (PDAs) and smart phones have increasingly become major consumers of remotely stored and/or streaming cloud content.
Thus, there is a need for improved resource allocation strategies that consider feedback from mobile users; and more particularly, there is a need for capturing and using mobile user feedback for more accurately determining service usage tendencies and for more accurately determining when peak demand is likely to occur.